Confluence

This connector is optimized for AI agents. For the data replication connector, see Confluence.

The Confluence agent connector is a Python package that equips AI agents to interact with Confluence through strongly typed, well-documented tools. It's ready to use directly in your Python app, in an agent framework, or exposed through an MCP.

Connector for the Confluence Cloud REST API. Provides read access to Confluence spaces, pages, blog posts, groups, and audit logs. Uses the Confluence Cloud REST API v2 for spaces, pages, and blog posts, and the v1 API for groups and audit records. Authenticates via HTTP Basic using an Atlassian account email and API token.

Example prompts

The Confluence connector is optimized to handle prompts like these.

List all spaces in my Confluence instance
Show me the most recently created pages
List all blog posts
Show me details for a specific page
List all groups in Confluence
Show me recent audit log entries
Get details about a specific space
Show me blog post details
Find pages created in the last 7 days
What spaces have the most pages?
Show me all pages in a specific space
Find blog posts by a specific author
What audit events happened this week?

Unsupported prompts

The Confluence connector isn't currently able to handle prompts like these.

Create a new page in Confluence
Update an existing page
Delete a space
Upload an attachment to a page
Manage space permissions

Entities and actions

This connector supports the following entities and actions. For more details, see this connector's full reference documentation.

Entity	Actions
Spaces	List, Get, Context Store Search
Pages	List, Get, Context Store Search
Blog Posts	List, Get, Context Store Search
Groups	List, Context Store Search
Audit	List, Context Store Search

Confluence API docs

See the official Confluence API reference.

SDK installation

uv pip install airbyte-agent-sdk

SDK usage

Connectors can run in hosted or open source mode.

Hosted

In hosted mode, API credentials are stored securely in Airbyte Agents. You provide your Airbyte credentials instead. If your Airbyte client can access multiple organizations, also set organization_id.

This example assumes you've already authenticated your connector with Airbyte. See Authentication to learn more about authenticating. If you need a step-by-step guide, see the hosted execution tutorial.

The connect() factory returns a fully typed ConfluenceConnector and reads AIRBYTE_CLIENT_ID / AIRBYTE_CLIENT_SECRET from the environment:

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector

connector = connect("confluence", workspace_name="<your_workspace_name>")

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector

connector = connect("confluence", workspace_name="<your_workspace_name>")

@tool
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector

connector = connect("confluence", workspace_name="<your_workspace_name>")

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ConfluenceConnector.tool_utils(framework="openai_agents")
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Confluence Assistant", tools=[confluence_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk import connect
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector

connector = connect("confluence", workspace_name="<your_workspace_name>")

mcp = FastMCP("Confluence Agent")

@mcp.tool
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Or pass credentials explicitly (equivalent, useful when you're not loading them from the environment):

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ConfluenceConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ConfluenceConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

@tool
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ConfluenceConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ConfluenceConnector.tool_utils(framework="openai_agents")
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Confluence Assistant", tools=[confluence_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector
from airbyte_agent_sdk.types import AirbyteAuthConfig

connector = ConfluenceConnector(
    auth_config=AirbyteAuthConfig(
        workspace_name="<your_workspace_name>",
        organization_id="<your_organization_id>",  # Optional for multi-org clients
        airbyte_client_id="<your-client-id>",
        airbyte_client_secret="<your-client-secret>"
    )
)

mcp = FastMCP("Confluence Agent")

@mcp.tool
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Open source

In open source mode, you provide API credentials directly to the connector.

Pydantic AI
LangChain
OpenAI Agents
FastMCP

Pydantic AI
from pydantic_ai import Agent
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector
from airbyte_agent_sdk.connectors.confluence.models import ConfluenceAuthConfig

connector = ConfluenceConnector(
    auth_config=ConfluenceAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Confluence API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

agent = Agent("openai:gpt-4o")

@agent.tool_plain
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    return await connector.execute(entity, action, params or {})

LangChain
from langchain_core.tools import tool
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector
from airbyte_agent_sdk.connectors.confluence.models import ConfluenceAuthConfig

connector = ConfluenceConnector(
    auth_config=ConfluenceAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Confluence API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

@tool
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    # connector.execute returns a Pydantic envelope for typed actions; fall back to raw data otherwise.
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

OpenAI Agents
from agents import Agent, function_tool
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector
from airbyte_agent_sdk.connectors.confluence.models import ConfluenceAuthConfig

connector = ConfluenceConnector(
    auth_config=ConfluenceAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Confluence API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

# strict_mode=False because `params: dict` is permissive and the default strict
# JSON schema rejects objects with additionalProperties.
@function_tool(strict_mode=False)
@ConfluenceConnector.tool_utils(framework="openai_agents")
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

agent = Agent(name="Confluence Assistant", tools=[confluence_execute])

FastMCP
from fastmcp import FastMCP
from airbyte_agent_sdk.connectors.confluence import ConfluenceConnector
from airbyte_agent_sdk.connectors.confluence.models import ConfluenceAuthConfig

connector = ConfluenceConnector(
    auth_config=ConfluenceAuthConfig(
        username="<Your Atlassian account email address>",
        password="<Your Confluence API token from https://id.atlassian.com/manage-profile/security/api-tokens>"
    )
)

mcp = FastMCP("Confluence Agent")

@mcp.tool
@ConfluenceConnector.tool_utils
async def confluence_execute(entity: str, action: str, params: dict | None = None):
    """Execute Confluence connector operations."""
    result = await connector.execute(entity, action, params or {})
    return result.model_dump(mode="json") if hasattr(result, "model_dump") else result

Authentication

For all authentication options, see the connector's authentication documentation.

Version information

Connector version: 1.0.1

Example prompts​

Unsupported prompts​

Entities and actions​

Confluence API docs​

SDK installation​

SDK usage​

Hosted​

Open source​

Authentication​

Version information​